import os

import torch
import torch.distributed as dist

from torch.nn import functional as F
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.cuda.amp import autocast, GradScaler

from data_utils import (
    TextAudioSpeakerLoader,
    TextAudioSpeakerCollate,
    DistributedBucketSampler,
)
from model.discriminators import (AVAILABLE_DURATION_DISCRIMINATOR_TYPES,
                                  MultiPeriodDiscriminator,
                                  MultiPeriodMultiResolutionDiscriminator,
                                  DurationDiscriminatorV1,
                                  DurationDiscriminatorV2,
                                  WavLMDiscriminator)
from model.flows import AVAILABLE_FLOW_TYPES
from model.models import SynthesizerTrn
from losses import (generator_loss,
                    discriminator_loss,
                    feature_loss,
                    kl_loss,
                    WavLMLoss)
from utils import commons, task
from utils.mel_processing import mel_spectrogram_torch, spec_to_mel_torch

torch.backends.cudnn.benchmark = False
global_step = 0


def main():
    hps = task.get_hparams()
    # Set random seed
    torch.manual_seed(hps.train.seed)
    global global_step
    # Initialize distributed
    world_size = int(os.environ.get('WORLD_SIZE', 1))
    local_rank = int(os.environ.get('LOCAL_RANK', 0))
    rank = int(os.environ.get('RANK', 0))
    torch.cuda.set_device(local_rank)
    dist.init_process_group("nccl")

    # Get the dataset and data loader
    train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
    train_sampler = DistributedBucketSampler(
        train_dataset,
        hps.train.batch_size,
        [32, 300, 400, 500, 600, 700, 800, 900, 1000],
        num_replicas=world_size,
        rank=rank,
        shuffle=True,
    )
    collate_fn = TextAudioSpeakerCollate()
    train_loader = DataLoader(
        train_dataset,
        num_workers=hps.num_workers,
        shuffle=False,
        pin_memory=True,
        collate_fn=collate_fn,
        batch_sampler=train_sampler,
    )
    if rank == 0:
        eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files,
                                              hps.data)
        eval_loader = DataLoader(
            eval_dataset,
            num_workers=hps.num_workers,
            shuffle=False,
            batch_size=hps.train.batch_size,
            pin_memory=True,
            drop_last=False,
            collate_fn=collate_fn,
        )

    # Get the tts model
    if ("use_mel_posterior_encoder" in hps.model.keys()
            and hps.model.use_mel_posterior_encoder):
        print("Using mel posterior encoder for VITS2")
        posterior_channels = hps.data.n_mel_channels  # vits2
        hps.data.use_mel_posterior_encoder = True
    else:
        print("Using lin posterior encoder for VITS1")
        posterior_channels = hps.data.filter_length // 2 + 1
        hps.data.use_mel_posterior_encoder = False

    # some of these flags are not being used in the code and directly set in hps
    # json file. they are kept here for reference and prototyping.
    if ("use_transformer_flows" in hps.model.keys()
            and hps.model.use_transformer_flows):
        use_transformer_flows = True
        transformer_flow_type = hps.model.transformer_flow_type
        print(f"Using transformer flows {transformer_flow_type} for VITS2")
        assert (
            transformer_flow_type in AVAILABLE_FLOW_TYPES
        ), f"transformer_flow_type must be one of {AVAILABLE_FLOW_TYPES}"
    else:
        print("Using normal flows for VITS1")
        use_transformer_flows = False

    if ("use_spk_conditioned_encoder" in hps.model.keys()
            and hps.model.use_spk_conditioned_encoder):
        if hps.data.n_speakers == 0:
            raise ValueError(
                "n_speakers must be > 0 when using spk conditioned encoder to",
                "train multi-speaker model")
        use_spk_conditioned_encoder = True
    else:
        print("Using normal encoder for VITS1")
        use_spk_conditioned_encoder = False

    if ("use_noise_scaled_mas" in hps.model.keys()
            and hps.model.use_noise_scaled_mas):
        print("Using noise scaled MAS for VITS2")
        use_noise_scaled_mas = True
        mas_noise_scale_initial = 0.01
        noise_scale_delta = 2e-6
    else:
        print("Using normal MAS for VITS1")
        use_noise_scaled_mas = False
        mas_noise_scale_initial = 0.0
        noise_scale_delta = 0.0

    if ("use_duration_discriminator" in hps.model.keys()
            and hps.model.use_duration_discriminator):
        # comment - choihkk
        # add duration discriminator type here
        # I think it would be a good idea to come up with a method to input this
        # part accurately, like a hydra
        duration_discriminator_type = getattr(hps.model,
                                              "duration_discriminator_type",
                                              "dur_disc_1")
        print(
            f"Using duration_discriminator {duration_discriminator_type} for",
            "VITS2")
        assert (duration_discriminator_type
                in AVAILABLE_DURATION_DISCRIMINATOR_TYPES)
        if duration_discriminator_type == "dur_disc_1":
            net_dur_disc = DurationDiscriminatorV1(
                hps.model.hidden_channels,
                hps.model.hidden_channels,
                3,
                0.1,
                gin_channels=hps.model.gin_channels
                if hps.data.n_speakers != 0 else 0,
            ).cuda(local_rank)
        elif duration_discriminator_type == "dur_disc_2":
            net_dur_disc = DurationDiscriminatorV2(
                hps.model.hidden_channels,
                hps.model.hidden_channels,
                3,
                0.1,
                gin_channels=hps.model.gin_channels
                if hps.data.n_speakers != 0 else 0,
            ).cuda(local_rank)
    else:
        print("NOT using any duration discriminator like VITS1")
        net_dur_disc = None

    if ("use_wd" in hps.model.keys() and hps.model.use_wd):
        os.makedirs(hps.model.slm_model, exist_ok=True)
        if not os.path.isfile(
            os.path.join(hps.model.slm_model, "pytorch_model.bin")
        ):
            task.download_wavlm_model(hps.model.slm_model)

        net_wd = WavLMDiscriminator(
            hps.model.slm_hidden,
            hps.model.slm_nlayers,
            hps.model.slm_initial_channel
        ).cuda(local_rank)

        wl = WavLMLoss(
            hps.model.slm_model,
            net_wd,
            hps.data.sampling_rate,
            hps.model.slm_sr,
        ).to(local_rank)
        print("Using WavLMDiscriminator")
    else:
        net_wd = None
        wl = None

    net_g = SynthesizerTrn(hps.data.num_phones,
                           posterior_channels,
                           hps.train.segment_size // hps.data.hop_length,
                           n_speakers=hps.data.n_speakers,
                           mas_noise_scale_initial=mas_noise_scale_initial,
                           noise_scale_delta=noise_scale_delta,
                           **hps.model).cuda(local_rank)
    if ("use_mrd_disc" in hps.model.keys()
            and hps.model.use_mrd_disc):
        print("Using MultiPeriodMultiResolutionDiscriminator")
        net_d = MultiPeriodMultiResolutionDiscriminator(
            hps.model.use_spectral_norm).cuda(local_rank)
    else:
        print("Using MPD")
        net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(local_rank)

    # Dispatch the model from cpu to gpu
    # comment - choihkk
    # if we comment out unused parameter like DurationDiscriminator's
    # self.pre_out_norm1,2 self.norm_1,2 and ResidualCouplingTransformersLayer's
    # self.post_transformer we don't have to set find_unused_parameters=True
    # but I will not proceed with commenting out for compatibility with the
    # latest work for others
    net_g = DDP(net_g, device_ids=[local_rank], find_unused_parameters=True)
    net_d = DDP(net_d, device_ids=[local_rank], find_unused_parameters=True)
    if net_dur_disc:
        net_dur_disc = DDP(
            net_dur_disc,
            device_ids=[local_rank],
            find_unused_parameters=True
        )
    if net_wd:
        net_wd = DDP(
            net_wd,
            device_ids=[local_rank],
            find_unused_parameters=True
        )

    # Get the optimizer
    optim_g = torch.optim.AdamW(
        net_g.parameters(),
        hps.train.learning_rate,
        betas=hps.train.betas,
        eps=hps.train.eps,
    )
    optim_d = torch.optim.AdamW(
        net_d.parameters(),
        hps.train.learning_rate,
        betas=hps.train.betas,
        eps=hps.train.eps,
    )
    if net_dur_disc:
        optim_dur_disc = torch.optim.AdamW(
            net_dur_disc.parameters(),
            hps.train.learning_rate,
            betas=hps.train.betas,
            eps=hps.train.eps,
        )
    else:
        optim_dur_disc = None
    if net_wd:
        optim_wd = torch.optim.AdamW(
            net_wd.parameters(),
            hps.train.learning_rate,
            betas=hps.train.betas,
            eps=hps.train.eps,
        )
    else:
        optim_wd = None

    # Load the checkpoint
    try:
        _, _, _, epoch_str = task.load_checkpoint(
            task.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
            optim_g)
        _, _, _, epoch_str = task.load_checkpoint(
            task.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
            optim_d)
        if net_dur_disc:
            _, _, _, epoch_str = task.load_checkpoint(
                task.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"),
                net_dur_disc,
                optim_dur_disc,
            )
        if net_wd:
            _, _, _, epoch_str = task.load_checkpoint(
                task.latest_checkpoint_path(hps.model_dir, "WD_*.pth"),
                net_wd,
                optim_wd,
            )
        global_step = (epoch_str - 1) * len(train_loader)
    except Exception as e:
        epoch_str = 1
        global_step = 0

    # Get the scheduler
    scheduler_g = torch.optim.lr_scheduler.ExponentialLR(
        optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
    scheduler_d = torch.optim.lr_scheduler.ExponentialLR(
        optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
    if net_dur_disc:
        scheduler_dur_disc = torch.optim.lr_scheduler.ExponentialLR(
            optim_dur_disc, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
    else:
        scheduler_dur_disc = None
    if net_wd:
        scheduler_wd = torch.optim.lr_scheduler.ExponentialLR(
            optim_wd, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
        )
    else:
        scheduler_wd = None

    # Get the tensorboard summary
    writer = None
    if rank == 0:
        logger = task.get_logger(hps.model_dir)
        logger.info(hps)
        writer = SummaryWriter(log_dir=hps.model_dir)
        writer_eval = SummaryWriter(
            log_dir=os.path.join(hps.model_dir, "eval"))

    scaler = GradScaler(enabled=hps.train.fp16_run)

    for epoch in range(epoch_str, hps.train.epochs + 1):
        if rank == 0:
            train_and_evaluate(
                rank,
                local_rank,
                epoch,
                hps,
                [net_g, net_d, net_dur_disc, net_wd, wl],
                [optim_g, optim_d, optim_dur_disc, optim_wd],
                [scheduler_g, scheduler_d, scheduler_dur_disc, scheduler_wd],
                scaler,
                [train_loader, eval_loader],
                logger,
                [writer, writer_eval],
            )
        else:
            train_and_evaluate(
                rank,
                local_rank,
                epoch,
                hps,
                [net_g, net_d, net_dur_disc, net_wd, wl],
                [optim_g, optim_d, optim_dur_disc, optim_wd],
                [scheduler_g, scheduler_d, scheduler_dur_disc, scheduler_wd],
                scaler,
                [train_loader, None],
                None,
                None,
            )
        scheduler_g.step()
        scheduler_d.step()
        if net_dur_disc:
            scheduler_dur_disc.step()
        if net_wd:
            scheduler_wd.step()

def train_and_evaluate(rank, local_rank, epoch, hps, nets, optims, schedulers, scaler,
                       loaders, logger, writers):
    net_g, net_d, net_dur_disc, net_wd, wl = nets
    optim_g, optim_d, optim_dur_disc, optim_wd = optims
    scheduler_g, scheduler_d, scheduler_dur_disc, scheduler_wd = schedulers
    train_loader, eval_loader = loaders
    if writers:
        writer, writer_eval = writers

    train_loader.batch_sampler.set_epoch(epoch)
    global global_step

    net_g.train()
    net_d.train()
    if net_dur_disc:
        net_dur_disc.train()
    if net_wd:
        net_wd.train()
    for batch_idx, (
            x,
            x_lengths,
            spec,
            spec_lengths,
            y,
            y_lengths,
            speakers,
    ) in enumerate(train_loader):
        if net_g.module.use_noise_scaled_mas:
            current_mas_noise_scale = (
                net_g.module.mas_noise_scale_initial -
                net_g.module.noise_scale_delta * global_step)
            net_g.module.current_mas_noise_scale = max(current_mas_noise_scale,
                                                       0.0)
        x, x_lengths = x.cuda(local_rank, non_blocking=True), x_lengths.cuda(
            local_rank, non_blocking=True)
        spec, spec_lengths = spec.cuda(
            local_rank, non_blocking=True), spec_lengths.cuda(local_rank,
                                                              non_blocking=True)
        y, y_lengths = y.cuda(local_rank, non_blocking=True), y_lengths.cuda(
            local_rank, non_blocking=True)
        speakers = speakers.cuda(local_rank, non_blocking=True)

        with autocast(enabled=hps.train.fp16_run):
            (
                y_hat,
                l_length,
                attn,
                ids_slice,
                x_mask,
                z_mask,
                (z, z_p, m_p, logs_p, m_q, logs_q),
                (hidden_x, logw, logw_),
            ) = net_g(x, x_lengths, spec, spec_lengths, speakers)

            if (hps.model.use_mel_posterior_encoder
                    or hps.data.use_mel_posterior_encoder):
                mel = spec
            else:
                # comment - choihkk
                # for numerical stable when using fp16 and torch>=2.0.0,
                # spec.float() could be help in the training stage
                # https://github.com/jaywalnut310/vits/issues/15
                mel = spec_to_mel_torch(
                    spec.float(),
                    hps.data.filter_length,
                    hps.data.n_mel_channels,
                    hps.data.sampling_rate,
                    hps.data.mel_fmin,
                    hps.data.mel_fmax,
                )
            y_mel = commons.slice_segments(
                mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
            y_hat_mel = mel_spectrogram_torch(
                y_hat.squeeze(1).float(),
                hps.data.filter_length,
                hps.data.n_mel_channels,
                hps.data.sampling_rate,
                hps.data.hop_length,
                hps.data.win_length,
                hps.data.mel_fmin,
                hps.data.mel_fmax,
            )

            y = commons.slice_segments(y, ids_slice * hps.data.hop_length,
                                       hps.train.segment_size)  # slice

            # Discriminator
            y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
            with autocast(enabled=False):
                loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
                    y_d_hat_r, y_d_hat_g)
                loss_disc_all = loss_disc
            # Duration Discriminator
            if net_dur_disc:
                y_dur_hat_r, y_dur_hat_g = net_dur_disc(
                    hidden_x.detach(), x_mask.detach(), logw_.detach(),
                    logw.detach())
                with autocast(enabled=False):
                    # TODO: I think need to mean using the mask, but for now,
                    # just mean all
                    (
                        loss_dur_disc,
                        losses_dur_disc_r,
                        losses_dur_disc_g,
                    ) = discriminator_loss(y_dur_hat_r, y_dur_hat_g)
                    loss_dur_disc_all = loss_dur_disc
                optim_dur_disc.zero_grad()
                scaler.scale(loss_dur_disc_all).backward()
                scaler.unscale_(optim_dur_disc)
                grad_norm_dur_disc = commons.clip_grad_value_(
                    net_dur_disc.parameters(), None)
                scaler.step(optim_dur_disc)

        optim_d.zero_grad()
        scaler.scale(loss_disc_all).backward()
        scaler.unscale_(optim_d)
        grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
        scaler.step(optim_d)

        if wl is not None and net_wd is not None:
            with autocast(enabled=hps.train.fp16_run, dtype=torch.float16):
                loss_slm = wl.discriminator(
                    y.detach().squeeze(), y_hat.detach().squeeze()
                ).mean()
            optim_wd.zero_grad()
            scaler.scale(loss_slm).backward()
            scaler.unscale_(optim_wd)
            grad_norm_wd = commons.clip_grad_value_(net_wd.parameters(), None)
            scaler.step(optim_wd)

        with autocast(enabled=hps.train.fp16_run):
            # Generator
            y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
            if net_dur_disc:
                y_dur_hat_r, y_dur_hat_g = net_dur_disc(
                    hidden_x, x_mask, logw_, logw)
            with autocast(enabled=False):
                loss_dur = torch.sum(l_length.float())
                loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
                loss_kl = kl_loss(z_p, logs_q, m_p, logs_p,
                                  z_mask) * hps.train.c_kl

                loss_fm = feature_loss(fmap_r, fmap_g)
                loss_gen, losses_gen = generator_loss(y_d_hat_g)
                loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl
                if net_dur_disc:
                    loss_dur_gen, losses_dur_gen = generator_loss(y_dur_hat_g)
                    loss_gen_all += loss_dur_gen
                if net_wd is not None:
                    loss_lm = wl(y.detach().squeeze(), y_hat.squeeze()).mean()
                    loss_lm_gen = wl.generator(y_hat.squeeze())
                    loss_gen_all += loss_lm
                    loss_gen_all += loss_lm_gen

        optim_g.zero_grad()
        scaler.scale(loss_gen_all).backward()
        scaler.unscale_(optim_g)
        grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
        scaler.step(optim_g)
        scaler.update()

        if rank == 0:
            if global_step % hps.train.log_interval == 0:
                lr = optim_g.param_groups[0]["lr"]
                losses = [
                    loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl
                ]
                logger.info("Train Epoch: {} [{:.0f}%]".format(
                    epoch, 100.0 * batch_idx / len(train_loader)))
                logger.info([round(x.item(), 5)
                             for x in losses] + [global_step, lr])

                scalar_dict = {
                    "loss/g/total": loss_gen_all,
                    "loss/d/total": loss_disc_all,
                    "learning_rate": lr,
                    "grad_norm_d": grad_norm_d,
                    "grad_norm_g": grad_norm_g,
                }
                if net_dur_disc:
                    scalar_dict.update({
                        "loss/dur_disc/total":
                        loss_dur_disc_all,
                        "grad_norm_dur_disc":
                        grad_norm_dur_disc,
                    })
                if net_wd:
                    scalar_dict.update(
                        {
                            "loss/wd/total": loss_slm.item(),
                            "loss/g/lm": loss_lm.item(),
                            "loss/g/lm_gen": loss_lm_gen.item(),
                            "grad_norm_wd": grad_norm_wd,
                        }
                    )
                scalar_dict.update({
                    "loss/g/fm": loss_fm,
                    "loss/g/mel": loss_mel,
                    "loss/g/dur": loss_dur,
                    "loss/g/kl": loss_kl,
                })

                scalar_dict.update({
                    "loss/g/{}".format(i): v
                    for i, v in enumerate(losses_gen)
                })
                scalar_dict.update({
                    "loss/d_r/{}".format(i): v
                    for i, v in enumerate(losses_disc_r)
                })
                scalar_dict.update({
                    "loss/d_g/{}".format(i): v
                    for i, v in enumerate(losses_disc_g)
                })

                image_dict = {
                    "slice/mel_org":
                    task.plot_spectrogram_to_numpy(
                        y_mel[0].data.cpu().numpy()),
                    "slice/mel_gen":
                    task.plot_spectrogram_to_numpy(
                        y_hat_mel[0].data.cpu().numpy()),
                    "all/mel":
                    task.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
                    "all/attn":
                    task.plot_alignment_to_numpy(attn[0,
                                                      0].data.cpu().numpy()),
                }
                task.summarize(
                    writer=writer,
                    global_step=global_step,
                    images=image_dict,
                    scalars=scalar_dict,
                )

            if global_step % hps.train.eval_interval == 0:
                evaluate(hps, net_g, eval_loader, writer_eval)
                task.save_checkpoint(
                    net_g,
                    optim_g,
                    hps.train.learning_rate,
                    epoch,
                    os.path.join(hps.model_dir,
                                 "G_{}.pth".format(global_step)),
                )
                task.save_checkpoint(
                    net_d,
                    optim_d,
                    hps.train.learning_rate,
                    epoch,
                    os.path.join(hps.model_dir,
                                 "D_{}.pth".format(global_step)),
                )
                if net_dur_disc:
                    task.save_checkpoint(
                        net_dur_disc,
                        optim_dur_disc,
                        hps.train.learning_rate,
                        epoch,
                        os.path.join(hps.model_dir,
                                     "DUR_{}.pth".format(global_step)),
                    )
                if net_wd:
                    task.save_checkpoint(
                        net_wd,
                        optim_wd,
                        hps.train.learning_rate,
                        epoch,
                        os.path.join(hps.model_dir, "WD_{}.pth".format(global_step)),
                    )
        global_step += 1

    if rank == 0:
        logger.info("====> Epoch: {}".format(epoch))


def evaluate(hps, generator, eval_loader, writer_eval):
    generator.eval()
    with torch.no_grad():
        for batch_idx, (
                x,
                x_lengths,
                spec,
                spec_lengths,
                y,
                y_lengths,
                speakers,
        ) in enumerate(eval_loader):
            x, x_lengths = x.cuda(0), x_lengths.cuda(0)
            spec, spec_lengths = spec.cuda(0), spec_lengths.cuda(0)
            y, y_lengths = y.cuda(0), y_lengths.cuda(0)
            speakers = speakers.cuda(0)

            # remove else
            x = x[:1]
            x_lengths = x_lengths[:1]
            spec = spec[:1]
            spec_lengths = spec_lengths[:1]
            y = y[:1]
            y_lengths = y_lengths[:1]
            speakers = speakers[:1]
            break
        y_hat, attn, mask, *_ = generator.module.infer(x,
                                                       x_lengths,
                                                       speakers,
                                                       max_len=1000)
        y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length

        if hps.model.use_mel_posterior_encoder or hps.data.use_mel_posterior_encoder:
            mel = spec
        else:
            mel = spec_to_mel_torch(
                spec,
                hps.data.filter_length,
                hps.data.n_mel_channels,
                hps.data.sampling_rate,
                hps.data.mel_fmin,
                hps.data.mel_fmax,
            )
        y_hat_mel = mel_spectrogram_torch(
            y_hat.squeeze(1).float(),
            hps.data.filter_length,
            hps.data.n_mel_channels,
            hps.data.sampling_rate,
            hps.data.hop_length,
            hps.data.win_length,
            hps.data.mel_fmin,
            hps.data.mel_fmax,
        )
    image_dict = {
        "gen/mel": task.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
    }
    audio_dict = {"gen/audio": y_hat[0, :, :y_hat_lengths[0]]}
    if global_step == 0:
        image_dict.update(
            {"gt/mel": task.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
        audio_dict.update({"gt/audio": y[0, :, :y_lengths[0]]})

    task.summarize(
        writer=writer_eval,
        global_step=global_step,
        images=image_dict,
        audios=audio_dict,
        audio_sampling_rate=hps.data.sampling_rate,
    )
    generator.train()


if __name__ == "__main__":
    main()
